agribusinessreview.comDECEMBER 202319collaborate with third-party partners to seamlessly integrate diverse on-farm data into our infrastructure, and figure out new technologies to facilitate this seamless integration of data in various platforms that can be extracted by research scientists and business units. Can you share some of the challenges you notice in integrating these technologies?It's a highly debatable topic, considering both perspectives. Consider I am a farmer. Farming is my sole livelihood, passed down through generations. I adhere to traditional practices, a prescribed approach in farming and crop protection. However, with the rise of focus on sustainability, emerging regulations demand reduced usage, posing a challenge. This raised the question of how to reduce the amount of crop protection products and maintain weed-free and disease-free fields. This is where the industry aims for a shift--leveraging IoT and remote sensing for site-specific applications, ensuring sustainability. It takes time as an industry to move forward and perfect the science at the same time, it is challenging to gain grower trust to adopt this evolving approach, promising not just ecological benefits but potentially greater financial sustainability in the long run. It's a collaborative journey for both sides to achieve this goal.What are some of the strategies you employ to adapt to these changes?In achieving solutions, technology plays a pivotal role, often involving remote sensing to detect and apply. Yet, success goes beyond technology--it's a nexus. Building trust and relationships with growers is crucial. The most successful instances occur when technology reaches the grower through trusted channels like agronomists or established sales channels. Gaining buy-in across the entire chain, from research to farm gate, is a lengthy process, requiring thorough research and the right value chain partners. This collaborative approach is key to the success of these solutions.How do you envision the future of this industry?In the last couple of months, the advent of large language models has sparked discussions on the universal applicability, including in AgTech. While these models offer solutions, the crux lies in data quality and standardization. The one with quality data will be the front-runner. But the biggest challenge lies in acquiring this data. Like any other space, data being the backbone of AI and ML models, is often siloed, lacking standards, leading to an 80-20 problem--80 percent time spent cleaning data, 20 percent using it. So implementing data standards can streamline processes, enhance data quality, and consistency, and allow interoperability between systems. Real-time data from fields is vital, but rural farming areas face connectivity challenges. Overcoming these hurdles, particularly ensuring quality, standardized data, is key to training and deploying effective models in AgTech. What is your piece of advice to your fellow peers?AgTech is a rapidly evolving space. And so I believe collaboration is the key. We should work together to ensure our data becomes interchangeable and interoperable. Regarding connectivity and storage, prioritize processing data on the edge to optimize cloud interactions, especially in areas with low connectivity. Maintain a strong focus on data quality, because, at the end of the day, data is king. Building trust in the data we collect is essential for leveraging it effectively in the intelligence we develop within this space. It takes time as an industry to move forward and perfect the science at the same time, it is challenging to gain grower trust to adopt this evolving approach, promising not just ecological benefits but potentially greater financial sustainability in the long run
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